In the document image processing arts, automatic orientation detection has drawn significant attention because the upside-down orientation detection of scanned pages is often required as a first step for further processing such as, for example, for image processing. Such analysis may be employed to improve automated page data extraction that includes text, barcodes, content, images, and the like. In order for a form to be processed using automated techniques, page orientation must be insured. When such form pages are not detected correctly, that particular form is rejected and must be manually keyed.
Many methods have arisen in this art to detect the orientation of a scanned document page. For example, one simple method of orientation detection is for an optical character recognition (OCR) system to read the page in both directions. The OCR results will likely be quite poor in one page direction and quite good in the other direction. One problem with this approach is that OCR is a costly operation in terms of computational time for many data processing centers where document throughput is high. However, OCR does provide relatively high accuracy rates.
Generally, methods for upside down page orientation detection can be classified into three broad categories. The first category utilizes the up/down asymmetry of passages of text or stroke distribution of the text. The second category applies Optical Character Recognition (OCR). When OCR is fed with a page with a wrong orientation, the error rate tends to be very high. In many cases, the OCR program module will notify a user accordingly. The correct orientation can be found by running the OCR in different orientations, and then selecting the orientation direction associated with the highest OCR confidence level. A third category, as disclosed in US. Publication No. 20090274392, to Fan et al., is based on selective character identification wherein certain simple characters such as letter “i” in lower case, and letter “T” in upper case are located and a given page's orientation determined from the orientation of those characters.
However, existing methods are not appropriate for different reasons when used in data processing center applications. The methods in the first category discussed above require a large amount of text letters to achieve statistical reliability. Also, they only work when text contains lower case characters. These two conditions are usually not satisfied in form documents. A full scale OCR usually provides enough accuracy. However, this method can be relatively expensive both in cost and in computing cycles and resources. The third method is more reliable but performance still tends to rely heavily on the number of characters located on a given page and on image quality. Unfortunately, both conditions cannot be assured. Many low image quality documents have extensive touch characters and/or many partial or broken characters thus causing incorrect orientation direction detection. In any case, the cost of an incorrectly oriented page in a high volume scan of hundreds or thousands of pages can be quite high in terms of time required for manual intervention/correction and overall customer dissatisfaction with the services being provided.
Accordingly, what is needed in this art are increasingly sophisticated systems and method for augmenting present methods used for determining the orientation direction automatically being detected of digital pages of a plurality of scanned documents in a digital document processing environment.